Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

Bricklayer

A Sentence Similarity Model Based on Word Embeddings and Dependency Syntax-Treeop words and perform morphological restoration. Then, some important operations will be performed, such as passive flipping, negative flipping, and so on. Finally, the similarity of two sentence pairs is calculated by weighting the block embeddings of the syntactic tree. Experiments show the effectiveness of this method.

期满

Semi-coupled Transform Learningtechnique has been applied in two problems. The first being image super-resolution and the second, cross lingual document retrieval. In both the cases, our proposed transform learning based formulation excels considerably over existing techniques.

Delirium

泥沼

Named Entity Disambiguation via Probabilistic Graphical Model with Embedding FeaturesRank algorithm are implemented for model parameters learning and inference. We evaluate . on existing dataset against several state-of-the-art NED systems, which validates the effectiveness of our proposed method.

笨拙的你

ALLAY

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Delving into Diversity in Substitute Ensembles and Transferability of Adversarial Exampleserability of crafted adversarial examples. Experimental results show that proposed ensemble adversarial attack strategies can successfully attack the DL system with ensemble adversarial training defense mechanism and the greater the diversity in substitute ensembles enables stronger transferability.

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0302-9743 , ICONIP 2018, held in Siem Reap, Cambodia, in December 2018..The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques acros

使人烦燥

fMRI Semantic Category Decoding Using Linguistic Encoding of Word Embeddingse spatial patterns of neural activation in the brain are correlated with thinking about different semantic categories of words (for example, tools, animals, and buildings) or when viewing the related pictures. In this paper, we present a computational model that learns to predict the neural activati

inhibit

Named Entity Disambiguation via Probabilistic Graphical Model with Embedding Features Wikipedia. State-of-the-art NED solutions harness neural networks to generate abstract representations, i.e., embeddings, of mentions and entities, based on which the disambiguation process can be achieved by finding entity with the most similar representation to mention. Nevertheless, the coherenc